Fast fusion moves for multi-model estimation

Authors:
Andrew Delong;Olga Veksler;Yuri Boykov
Affiliations:
University of Western Ontario, Canada;University of Western Ontario, Canada;University of Western Ontario, Canada
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Year:
2012

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Abstract

We develop a fast, effective algorithm for minimizing a well-known objective function for robust multi-model estimation. Our work introduces a combinatorial step belonging to a family of powerful move-making methods like α-expansion and fusion. We also show that our subproblem can be quickly transformed into a comparatively small instance of minimum-weighted vertex-cover. In practice, these vertex-cover subproblems are almost always bipartite and can be solved exactly by specialized network flow algorithms. Experiments indicate that our approach achieves the robustness of methods like affinity propagation, whilst providing the speed of fast greedy heuristics.